朱保锋, 苏小玲. 大型网络异常数据库的快速数据定位模型仿真[J]. 微电子学与计算机, 2016, 33(2): 142-145.
引用本文: 朱保锋, 苏小玲. 大型网络异常数据库的快速数据定位模型仿真[J]. 微电子学与计算机, 2016, 33(2): 142-145.
ZHU Bao-feng, SU Xiao-ling. Rapid Data Location Model Simulation of Abnomal Database of Large Network[J]. Microelectronics & Computer, 2016, 33(2): 142-145.
Citation: ZHU Bao-feng, SU Xiao-ling. Rapid Data Location Model Simulation of Abnomal Database of Large Network[J]. Microelectronics & Computer, 2016, 33(2): 142-145.

大型网络异常数据库的快速数据定位模型仿真

Rapid Data Location Model Simulation of Abnomal Database of Large Network

  • 摘要: 在对大型网络异常数据库快速数据定位的研究过程中, 采用当前的算法进行快速数据定位时无法精确地提取特定的异常数据特征, 存在数据定位精确度低的问题.对此提出基于改进多子群萤火虫算法的大型网络异常数据库的快速数据定位建模方法.先组建大型网络异常数据库的实体模型, 并以组建的模型为依据详细地分析异常数据形成的原因, 同时提取网络异常数据库中异常数据的特征, 融入多子群萤火虫算法, 获得大型网络异常数据库中特定异常数据特征, 并根据其特征建立大型网络异常数据库的快速数据定位模型.实验仿真证明, 基于改进多子群萤火虫算法的大型网络异常数据库的快速数据定位建模方法定位精确度高, 适应性强.

     

    Abstract: On rapid data of large-scale network abnormal database localization in the process of research, using the current algorithm when positioning rapid data cannot be accurately extract the specific characteristics of abnormal data exist the problem of low positioning accuracy data. Is proposed based on improved fertility group of firefly algorithm of large fast positioning data modeling method of network anomaly database. To form a large network anomaly database entity model, and on the basis of the model to form a detailed analysis of the causes of the formation of abnormal data, extracting network anomaly characteristics of the abnormal data in the database at the same time, the fertility group of firefly algorithm, to obtain large network anomaly characteristics of the specific abnormal data in the database, and according to its characteristics to establish a large database of power network fault positioning rapid data model. Experimental simulation show that the firefly algorithm based on improved fertility group of large-scale network abnormal database orientation of rapid data modeling method of high precision, strong adaptability.

     

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